CN109858711B - Wind power consumption day-ahead scheduling method considering price type demand response and participation of CSP power station - Google Patents
Wind power consumption day-ahead scheduling method considering price type demand response and participation of CSP power station Download PDFInfo
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Abstract
The invention relates to a day-ahead wind power consumption scheduling method considering price type demand response and CSP power station participation, aiming at the serious problem of wind abandon caused by weak power grid regulation capacity and single price mechanism of a power market, the invention considers the uncertainty of wind power prediction and price type demand response and system safety constraint on the premise of considering comprehensive cost, constructs a scheduling model participating in wind power consumption based on the CSP power station and the price type demand response, combines the CSP power station and the price type demand response with wind power optimization scheduling, and improves the wind power consumption capacity of the system through coordinated scheduling on two sides of source load.
Description
Technical Field
The invention relates to a day-ahead wind power consumption scheduling method considering price type demand response and participation of a CSP power station.
Background
The renewable energy sources such as wind, light and the like in northwest areas of China are rich, and multi-source complementation becomes a main characteristic of a local new energy power system. However, the wind abandoning phenomenon is increasingly serious due to the comprehensive influence of technical and policy factors such as strong wind wave mobility, weak power grid regulation capability, low load level, single price mechanism of the current power market and the like. How to consider technical and market factors, reduce the amount of abandoned wind power and improve the wind power consumption level is a practical problem which needs to be researched and solved urgently in a large-scale wind power system.
By strengthening a demand side management and user response system (price-based demand response, PDR), the wind power consumption capacity can be improved; meanwhile, renewable energy sources with regulation capacity can be fully utilized for generating electricity, such as: solar photo-thermal power (CSP) realizes output power stabilization and complementation to fluctuating power supplies such as wind power and the like, and further improves the internet surfing space of the wind power. In fact, renewable energy sources with strong scheduling can be utilized on the power supply side, such as: the CSP power station makes up the deficiency of the wind power output anti-peak-shaving characteristic, and improves the wind power consumption together through source-load coordination in cooperation with peak shaving and valley filling of the load side price type demand response.
Disclosure of Invention
The invention aims to solve the technical problem of providing a wind power consumption day-ahead scheduling method which is scientific, reasonable, high in applicability and good in effect and takes price type demand response and CSP power station participation into consideration. The model combines the CSP power station and the price type demand response with the wind power optimization scheduling, and improves the wind power consumption capacity of the system through the coordinated scheduling of the source load and the load.
The technical scheme adopted for solving the technical problem is as follows: a wind power consumption day-ahead scheduling method considering price type demand response and CSP power station participation is characterized in that wind power prediction and price type demand response are considered together with uncertainty and system safety constraint on the premise of considering comprehensive cost, and a scheduling model based on CSP power station and price type demand response participation wind power consumption is constructed; the model combines a CSP power station and a price type demand response with wind power optimization scheduling, improves the capacity of a system for absorbing wind power through coordinated scheduling of two sides of a source load, and specifically comprises the following steps:
1) uncertainty analysis of price type demand response and wind power forecast
(a) Uncertainty of price type demand response
In price type demand response, according to the psychological principle of consumers, when a dispatching center makes a day-ahead dispatching plan, the electricity utilization habits of users are changed to a certain extent by means of real-time electricity prices with time scales of hour levels, and the purposes of peak clipping, valley filling and wind power internet space increasing are achieved;
in the price type demand response, the influence of the electricity price change rate on the load response rate is expressed by using an elasticity coefficient, and the load response rate for a T period is modeled as an expression (1):
wherein: phi is aΔq，tFor the response rate of the load for the period t,
the change rate of the electricity price of the load in the time interval T, and T is equal to (1: T); e is a price type demand elasticity matrix expressed as formula (2):
wherein: epsiloniiCoefficient of self-elasticity,. epsilonijFor cross elasticity coefficients, subscripts i and j denote the ith and jth scheduling periods, respectively;
the uncertainty degree of the load response rate is summarized by adopting a triangular membership function, and a fuzzy expression of the load response rate and a membership parameter have the following relationship and are calculated as a formula (3):
φΔq，t＝(φΔql，t，φΔq2，t，φΔq3，t) (3)
wherein:
is phiΔq，tThe fuzzy expression of (1);
φΔq1，t，φΔq2，t，φΔq3，tmembership degree parameter of load response rate in t time period;
δtfor the maximum error level of the predicted load response rate at time t, it is calculated as (5):
wherein: lambda [ alpha ]1A proportionality coefficient before the electricity price factor is dominant;
is the inflection point of the rate of change of electricity price;
the load change rate corresponding to the electricity price change rate inflection point;
the maximum/minimum value of the rate of change of the electricity price;
(b) uncertainty of wind power prediction
Wind power is a power supply which has volatility in output power and is difficult to control, and is a typical intermittent and low-power-density power supply, and wind power prediction often has a certain prediction error; the uncertainty of the prediction error is described by a normal distribution, which is expressed as formula (6):
wherein: omegatWind power prediction error at the time t; n represents that the wind power prediction error obeys normal distribution;
predicting power for the wind power at the t moment; wmThe installed capacity of the wind power plant;
the actual output of the wind power plant is the sum of the predicted output of the wind power plant and the prediction error, and meanwhile, the actual output of the wind power plant does not exceed the loading capacity of the wind power plant, and the calculation is as the formula (7):
wherein:
real output of the wind power plant at the moment t is obtained;
2) establishment of CSP power station heat-electricity conversion model
The solar mirror field collects heat energy generated by solar radiation to the heat collection tower, and the heat energy collected by the heat collection tower is calculated as the formula (8):
wherein:
the heat energy collected by the heat collection tower at the moment t is obtained; etad-thTo the light-to-heat conversion efficiency; sSFIs the area of a solar mirror field;
Dtrepresents the solar direct radiation index (DNI) at time t;
the heat energy collected by the heat collecting tower can be stored in the heat storage device through the heat transfer fluid and also can be directly supplied to a power generation system for power generation, and the heat energy collected by the heat collecting tower has the possibility of being abandoned in order to ensure the stable operation of the CSP power station; meanwhile, the heat energy used for power generation of the power generation system can also be supplemented by the heat storage system, and is expressed as the formula (9):
wherein:
representing the heat storage device heat release efficiency;
represents the heat energy rejected by the heat collection tower at the moment t; eSURepresents the thermal energy consumed by the power generation system during startup; u. oftA start variable indicating the power generation system, 1 indicating that the power generation system is started at that time;
the heat storage amount of the heat storage device at each time is expressed by the following formula (10):
wherein:
respectively representing the heat storage amount of the heat storage device at the moment t and the moment t-1; etalossIndicating a rate of heat loss from the heat storage device;
represents the charging efficiency of the heat storage device;
representing the charging power of the heat storage device;
representing the heat release power of the heat storage device; Δ t represents a time interval;
the thermodynamic dynamic differential equation is not considered in the static model of the CSP power station, and the power generation power and the heat energy consumption power of the CSP power station can be regarded as a linear relation and are calculated as the formula (11):
wherein:
representing the generated power of the CSP power station for supplying load at the time t; etaRs,uThe positive rotation standby coefficient of the CSP power station;
3) establishment of day-ahead scheduling model considering PDR and CSP power station participating in wind power consumption
a) Objective function
The method comprises the following steps of comprehensively considering the power generation cost of a conventional thermal power generating unit, the operation and maintenance cost of a wind power and CSP power station, the renewable energy environmental benefit brought by grid-connected consumption, the cost of the thermal power generating unit and the CSP power station participating in load standby and wind power prediction error standby, establishing a combined scheduling model containing the wind power, the CSP power station and the thermal power station and based on optimal cost, and calculating a target function as a formula (12):
min F＝D1+D2+D3+D4-D5 (12)
wherein: f is the comprehensive cost of the system when thermal power, wind power and CSP power stations participate in the optimized dispatching; d1The power generation cost when the load is supplied to the thermal power generating unit; d2The operation and maintenance cost of the wind power is reduced; d3The operation and maintenance cost for supplying power to the load by the CSP power station; d4The cost for the thermal power generating unit and the CSP power station to participate in load and wind power prediction error standby is saved; d5Environmental benefits brought to the power generation grid-connected consumption of wind power and CSP power stations;
the output of the thermal power generating unit is flexible and controllable, and the stable operation of a power grid can be ensured through reasonable optimized scheduling; in order to meet the load requirement in the scheduling process, the output is often required to be adjusted, even the unit is scheduled to be started and stopped, the power generation cost at the moment mainly comprises the coal consumption cost of the thermal power unit and the start and stop cost of the unit, and the calculation is as the formulas (13) and (14):
fi(PGi,t)＝aiPGi,t 2+biPGi,t+ci (14)
wherein: t is the total duration; n is a radical ofGThe number of the thermal power generating units is; f. ofiThe coal consumption cost of the thermal power generating unit i is obtained; pGi,tThe output of the thermal power generating unit i in the time period t is obtained; v isi,tV and vi,t-1Respectively the running states of the thermal power generating unit i at t and t-1, if vi,tV denotes unit operation as 1i,tWhen the unit is stopped, 0 represents the unit is stopped; siThe start-stop cost of the unit i is calculated; a isi，bi，ciThe coal consumption cost parameter is the coal consumption cost parameter of the thermal power generating unit i;
wind power generation belongs to renewable energy power generation, coal is not consumed in the power generation process, but certain operation and maintenance cost can be generated in the power generation process of a fan due to uncertainty of wind speed; the operation maintenance cost of the wind power and the wind power output power can be approximately regarded as a linear relation, and the linear relation is calculated as an expression (15):
wherein: kWOperating and maintaining costs for the wind farm; pW,tGenerating power of the wind power plant at the time t;
the CSP power station operation and maintenance cost is approximately regarded as a linear function of the generated power, and is calculated as the formula (16):
wherein: kCSPAs a sheet of CSP power stationA bit run maintenance cost;
in order to deal with uncertainty and emergency of load and wind power prediction, a certain rotation reserve capacity needs to be reserved, and the reserve cost of a thermal power unit and a CSP power station is calculated as a formula (17):
wherein:
indicating the participation of the CSP power station in the load standby,
Representing the cost coefficient of the CSP power station participating in wind power standby;
wind power, CSP power station renewable energy are incorporated into the power networks and are consumed and have reduced thermal power unit's the power generation on-line volume, effectively reduce the emission of pollutants such as sulfur and sodium sulfate, can bring environmental benefit, calculate (18) formula:
wherein: pW,tPower is consumed for the grid connection of the wind power plant at the time t; rhoCSPEnvironmental benefit coefficient brought to CSP power station grid-connected consumption; rhoWEnvironmental benefit coefficient brought to wind power grid connection consumption;
b) system constraints
When the network transmission loss is not counted, the sum of the output power of each unit is equal to the value after the power grid load response change, and is expressed as a formula (19):
wherein: l istThe load power value before the system needs to respond at the time t;
the transmission capacity constraint of the transmission line is expressed as formula (21):
wherein: pij,maxIs the maximum transmission capacity of the transmission line between nodes i and j; b isijIs the susceptance between nodes ij; thetai,tIs the voltage phase angle of node i; thetaj,tIs the voltage phase angle of node j;
the opportunity constraint is adopted to determine the wind power reserve capacity, the wind power reserve cost is reduced on the basis of ensuring the safety of a power grid, and the concrete constraint is expressed as a formula (22):
wherein:
the positive rotation reserve capacity of the thermal power generating unit i at the moment t is obtained;
the negative rotation reserve capacity of the thermal power generating unit i at the moment t is obtained;
positive rotation reserve capacity provided for CSP power station at time t,
The negative spinning reserve capacity provided by the CSP plant at time t,
μLis the load reserve factor; alpha and beta are respectively confidence coefficients meeting the positive and negative rotation standby constraints;
the heat storage device of the CSP power station has to be within the rated limit range, and the heat storage device cannot be simultaneously charged/discharged in each scheduling period, and the specific constraint is expressed as the following formula (23) and formula (24):
wherein:
the maximum charging power of the heat storage system;
the maximum heat release power of the heat storage system;
the heat storage quantity of the CSP power station heat storage device is expressed as a formula (25):
wherein:
the minimum heat storage quantity of the heat storage device is obtained; xiTSThe maximum heat storage capacity of the heat storage device is expressed by taking FLH as a unit;
the specific constraint of the maximum/minimum output of the thermal power generating unit is expressed as a formula (26):
UGi,tPi，min≤PGi,t≤UGi,tPi，max (26)
wherein: u shapeGi,tThe method comprises the following steps that 1 represents operation, and 0 represents shutdown, wherein the thermal power generating unit i is in an operating state; pi,minThe minimum output of the thermal power generating unit i is obtained; p isi,maxThe maximum output of the thermal power generating unit i is obtained;
the ramp rate constraint expression of the thermal power generating unit is as follows (27):
-rdi≤PGi,t-PGi,t-1≤rui (27)
during day-ahead scheduling, the wind power on-line power cannot exceed the predicted value, and the specific expression is (28):
wherein:
for wind farms at time tThe predicted output of (2);
the sum of the expected load response values after the demand response is zero in the whole scheduling period, and is expressed as formula (29):
considering the benefit of the user, the load variation needs to be limited, and the satisfaction of the user power utilization mode and the satisfaction of the power utilization expense are used as measurement indexes and expressed as formulas (30) and (31):
wherein:
the minimum value of the satisfaction degree of the user power utilization mode is set;
the minimum value of the satisfaction degree of electricity cost expenditure;
the load value after the price type demand response is between the upper limit and the lower limit of the load value before the response, and the specific constraint expression is (33):
Lmin-ηminLt≤E(Δqt)≤Lmax-ηmaxLt (33)
wherein: l ismaxIs the maximum value, L, of the original load curveminIs the minimum value of the original load curve; etamax、ηminTaking the value eta as the peak-to-valley difference coefficient of the demand response loadmax≥1，ηmin≤1。
The effect of the day-ahead wind power consumption scheduling method considering the price type demand response and the participation of the CSP power station is represented as follows: on the premise of considering the comprehensive cost, the uncertainty of wind power prediction and price type demand response and system safety constraints are considered, a scheduling model based on the CSP power station and the price type demand response participating in wind power consumption is constructed, and the capacity of the system for consuming wind power is improved through coordinated scheduling of the source load and the load. The method is scientific and reasonable, and has strong applicability and good effect.
Drawings
FIG. 1 is a 30-node wiring diagram of the modified IEEE;
FIG. 2CSP power plant components and energy flow diagrams;
FIG. 3 illustrates the pre-and post-response loads of the price type demand and the performance diagrams of various types of units;
FIG. 4 is a real-time electricity price graph for each dispatch period after a price type demand response;
FIG. 5 illustrates the heat storage device for heat and charge conditions and the amount of heat stored during each scheduled time period;
FIG. 6CSP power station thermal power consumption per period.
Detailed Description
The present invention will be further explained with reference to the drawings and embodiments, in which a method for scheduling wind power generation in consideration of the price type demand response and participation of CSP power stations in the day ahead is described.
The invention relates to a day-ahead wind power consumption scheduling method considering price type demand response and participation of a CSP power station, which is characterized in that uncertainty and system safety constraint of wind power prediction and price type demand response are considered on the premise of considering comprehensive cost, and a scheduling model based on CSP power station and price type demand response participation wind power consumption is constructed; the model combines a CSP power station and a price type demand response with wind power optimization scheduling, improves the wind power consumption capability of the system through coordinated scheduling of source load two sides, and specifically comprises the following steps:
1) uncertainty analysis of price type demand response and wind power forecast
(a) Uncertainty of price type demand response
In price type demand response, according to the psychological principle of consumers, when a dispatching center makes a day-ahead dispatching plan, the electricity utilization habits of users are changed to a certain extent by means of real-time electricity prices with time scales of hour levels, and the purposes of peak clipping, valley filling and wind power internet space increasing are achieved;
in the price type demand response, the influence of the electricity price change rate on the load response rate is expressed by using an elasticity coefficient, and the load response rate for a T period is modeled as an expression (1):
wherein: phi is aΔq,tFor the response rate of the load for the period t,
the change rate of the electricity price of the load in the time interval T, and T is equal to (1: T);
e is a price type demand elasticity matrix expressed as formula (2):
wherein: epsiloniiCoefficient of self-elasticity,. epsilonijFor cross elasticity coefficients, subscripts i and j denote the ith and jth scheduling periods, respectively;
the uncertainty degree of the load response rate is summarized by adopting a triangular membership function, and a fuzzy expression of the load response rate and a membership parameter have the following relationship and are calculated as a formula (3):
φΔq,t＝(φΔq1,t，φΔq2,t，φΔq3,t) (3)
wherein:
is phiΔq,tThe fuzzy expression of (1);
φΔq1,t，φΔq2,t，φΔq3,tmembership parameter of load response rate in t time period;
δtfor the maximum error level of the predicted load response rate at time t, it is calculated as (5):
wherein: lambda [ alpha ]1A proportionality coefficient before the electricity price factor is dominant;
is the inflection point of the rate of change of electricity price;
the load change rate corresponding to the electricity price change rate inflection point;
the maximum/minimum value of the rate of change of the electricity price;
(b) uncertainty of wind power prediction
Wind power is a power supply which has volatility in output power and is difficult to control, and is a typical intermittent and low-power-density power supply, and wind power prediction often has a certain prediction error; the uncertainty of the prediction error is described by a normal distribution, which is expressed as formula (6):
wherein: omegatWind power prediction error at the time t; n represents that the wind power prediction error obeys normal distribution;
predicting the power of the wind power at the time t; wmIs the installed capacity of the wind farm;
the actual output of the wind power plant is the sum of the predicted output of the wind power plant and the prediction error, and meanwhile, the actual output of the wind power plant does not exceed the loading capacity of the wind power plant, and the calculation is as the formula (7):
wherein:
real output of the wind power plant at the time t is obtained;
2) establishment of CSP power station heat-electricity conversion model
The solar mirror field collects heat energy generated by solar radiation to the heat collection tower, and the heat energy collected by the heat collection tower is calculated as the formula (8):
wherein:
the heat energy collected by the heat collection tower at the moment t is obtained; etad-thTo the light-to-heat conversion efficiency; sSFIs the area of a solar mirror field;
Dtrepresents the solar direct radiation index (DNI) at time t;
the heat energy collected by the heat collecting tower can be stored in the heat storage device through the heat transfer fluid and also can be directly supplied to a power generation system for power generation, and the heat energy collected by the heat collecting tower has the possibility of being abandoned in order to ensure the stable operation of the CSP power station; meanwhile, the heat energy used for power generation of the power generation system can also be supplemented by the heat storage system, and is expressed as the formula (9):
wherein:
represents the heat-storage device heat-release efficiency;
represents the heat energy rejected by the heat collection tower at the moment t; eSURepresents the thermal energy consumed by the power generation system during startup; u. oftA start variable indicating the power generation system, 1 indicating that the power generation system is started at that time;
the heat storage amount of the heat storage device at each time is expressed by the following formula (10):
wherein:
respectively representing the heat storage amount of the heat storage device at the moment t and the moment t-1; etalossIndicating a rate of heat loss from the heat storage device;
represents the charging efficiency of the heat storage device;
representing the charging power of the heat storage device;
representing the heat release power of the heat storage device; Δ t represents a time interval;
the thermodynamic dynamic differential equation is not considered in the static model of the CSP power station, and the power generation power and the heat energy consumption power of the CSP power station can be regarded as a linear relation and are calculated as the formula (11):
wherein:
representing the generated power of the CSP power station for supplying load at the time t; etaRs,uThe positive rotation standby coefficient of the CSP power station;
3) establishment of day-ahead scheduling model considering participation of PDR (plant data Rate) and CSP (compact strip production) power station in wind power consumption
a) Objective function
The method comprises the following steps of comprehensively considering the power generation cost of a conventional thermal power generating unit, the operation and maintenance cost of a wind power and CSP power station, the renewable energy environmental benefit brought by grid-connected consumption, the cost of the thermal power generating unit and the CSP power station participating in load standby and wind power prediction error standby, establishing a combined scheduling model containing the wind power, the CSP power station and the thermal power station and based on optimal cost, and calculating a target function as a formula (12):
min F＝D1+D2+D3+D4-D5 (12)
wherein: f is the comprehensive cost of the system when the thermal power, wind power and CSP power stations participate in the optimized scheduling; d1The power generation cost when the load is supplied to the thermal power generating unit; d2The operation and maintenance cost of the wind power is reduced; d3The operation and maintenance cost for supplying power to the load by the CSP power station; d4The cost for the thermal power generating unit and the CSP power station to participate in load and wind power prediction error standby is saved; d5Environmental benefits brought to the power generation grid-connected consumption of wind power and CSP power stations;
the output of the thermal power generating unit is flexible and controllable, and the stable operation of a power grid can be ensured through reasonable optimized scheduling; in order to meet the load requirement in the scheduling process, the output is often required to be adjusted, even the unit is scheduled to be started and stopped, the power generation cost at the moment mainly comprises the coal consumption cost of the thermal power unit and the start and stop cost of the unit, and the calculation is as the formulas (13) and (14):
fi(PGi,t)＝aiPGi,t 2+biPGi,t+ci (14)
wherein: t is the total duration; n is a radical ofGThe number of the thermal power generating units is; f. ofiThe coal consumption cost of the thermal power generating unit i is obtained; pGi,tThe output of the thermal power generating unit i in the time period t is obtained; v isi,tV and vi,t-1Respectively the running states of the thermal power generating unit i at t and t-1, if vi,tV denotes unit operation as 1i,tWhen the unit is stopped, 0 represents the unit is stopped; siThe start-stop cost of the unit i is calculated; a isi，bi，ciThe coal consumption cost parameter is the coal consumption cost parameter of the thermal power generating unit i;
wind power generation belongs to renewable energy power generation, coal is not consumed in the power generation process, but certain operation and maintenance cost can be generated in the power generation process of a fan due to uncertainty of wind speed; the operation and maintenance cost of the wind power and the wind power output power can be approximately regarded as a linear relation, and are calculated as an expression (15):
wherein: k isWOperating and maintaining costs for the wind farm; p isW,tGenerating power of the wind power plant at the time t;
the operation and maintenance cost of the CSP power station is approximately regarded as a linear function of the generated power, and is calculated as the formula (16):
wherein: kCSPThe unit operation and maintenance cost of the CSP power station is saved;
in order to deal with uncertainty and emergency of load and wind power prediction, a certain rotation reserve capacity needs to be reserved, and the reserve cost of a thermal power unit and a CSP power station is calculated as a formula (17):
wherein:
indicating the participation of the CSP power station in the load standby,
Representing the cost coefficient of the CSP power station participating in wind power standby;
wind power, CSP power station renewable energy are incorporated into the power networks and are consumed and have reduced thermal power unit's the power generation on-line volume, effectively reduce the emission of pollutants such as sulfur and sodium sulfate, can bring environmental benefit, calculate (18) formula:
wherein: pW,tPower is consumed for the grid connection of the wind power plant at the time t; rhoCSPEnvironmental benefit coefficient brought to CSP power station grid-connected consumption; rhoWEnvironmental benefit coefficient brought to wind power grid connection consumption;
b) system constraints
When the network transmission loss is not counted, the sum of the output power of each unit is equal to the value after the power grid load response change, and is expressed as a formula (19):
wherein: l istThe load power value before the system needs to respond at the time t;
the transmission capacity constraint of the transmission line is expressed as formula (21):
wherein: pij,maxIs the maximum of the transmission line between nodes i and jA transmission capacity; b isijIs the susceptance between nodes ij; thetai,tIs the voltage phase angle of node i; thetaj,tIs the voltage phase angle of node j;
the opportunity constraint is adopted to determine the wind power reserve capacity, the wind power reserve cost is reduced on the basis of ensuring the safety of a power grid, and the concrete constraint is expressed as a formula (22):
wherein:
the positive rotation reserve capacity of the thermal power generating unit i at the moment t is obtained;
the negative rotation reserve capacity of the thermal power generating unit i at the moment t is obtained;
positive rotation reserve capacity provided for CSP power station at time t,
The negative spinning reserve capacity provided by the CSP plant at time t,
μLis the load reserve factor; alpha and beta are respectively confidence coefficients meeting the positive and negative rotation standby constraints;
the heat storage device of the CSP power station has to be within the rated limit range, and the heat storage device cannot be simultaneously charged/discharged in each scheduling period, and the specific constraint is expressed as the following formula (23) and formula (24):
wherein:
the maximum charging power of the heat storage system;
the maximum heat release power of the heat storage system;
the heat storage quantity of the CSP power station heat storage device is expressed as a formula (25):
wherein:
the minimum heat storage quantity of the heat storage device is obtained; xi shapeTSThe maximum heat storage capacity of the heat storage device is expressed by taking FLH as a unit;
the specific constraint of the maximum/minimum output of the thermal power generating unit is expressed as a formula (26):
UGi,tPi，min≤PGi,t≤UGi,tPi，max (26)
wherein: u shapeGi,tThe method comprises the following steps that 1 represents operation, and 0 represents shutdown, wherein the thermal power generating unit i is in an operating state; pi,minThe minimum output of the thermal power generating unit i is obtained; pi,maxThe maximum output of the thermal power generating unit i is obtained;
the ramp rate constraint expression of the thermal power generating unit is as follows (27):
-rdi≤PGi,t-PGi,t-1≤rui (27)
during day-ahead scheduling, the wind power on-line power cannot exceed the predicted value, and the specific expression is (28):
wherein:
the predicted output of the wind power plant at the time t is obtained;
the sum of the expected load response values after the demand response is zero in the whole scheduling period, and is expressed as formula (29):
considering the benefit of the user, the load variation needs to be limited, and the satisfaction of the user power utilization mode and the satisfaction of the power utilization expense are used as measurement indexes and expressed as formulas (30) and (31):
wherein:
the minimum value of the satisfaction degree of the power utilization mode of the user is set;
the minimum value of the satisfaction degree of electricity cost expenditure;
the load value after the price type demand response is between the upper limit and the lower limit of the load value before the response, and the specific constraint expression is (33):
Lmin-ηminLt≤E(Δqt)≤Lmax-ηmaxLt (33)
wherein: l ismaxIs the maximum value, L, of the original load curveminIs the minimum value of the original load curve; etamax、ηminTaking the value eta as the peak-to-valley difference coefficient of the demand response loadmax≥1，ηmin≤1。
The embodiment takes an improved IEEE-30 node system as an example, a CSP power station and a price type demand response are calculated by combining with wind power optimization scheduling, the system comprises 6 conventional thermal power units, a wind power plant and a CSP power station, and a system wiring diagram is shown in figure 1; the data are from measured data of wind power plants and CSP power plants, and the data can be obtained by using a data acquisition device of a commercial product familiar to the technical personnel in the field. The CSP plant main parameters are shown in table 1.
Example the calculation conditions are illustrated below:
1) the electricity price before price type demand response is 400 yuan/MWh;
2) coefficient of proportionality λ1＝0.5；
3) Inflection point of electricity price change rate
The value is +/-0.3, and the maximum/minimum value of the electricity price change rate is +/-0.5;
4) load reserve factor μLThe value is 0.1;
5) cost of CSP plant participation in load and wind backup
6) Cost coefficient K of CSP power station power generation operation maintenanceCSP40 yuan/MW;
7) wind power operation maintenance cost KWIs 30 yuan/MW;
8) environmental benefit coefficient rho generated by grid-connected consumption of wind power and CSP power stationCSP＝ρW120 yuan/WM;
9) the cost coefficient pi G L-pi G W-120 yuan/MW of the thermal power generating unit participating in load and wind power backup;
10) minimum value of satisfaction degree of user power utilization mode
The value is 0.9, and the minimum value of the satisfaction degree of electricity cost expenditure
The value is 1;
10) peak to valley difference coefficient ηmax、ηminThe values are 1.05 and 0.95 respectively;
11) the confidence alpha is 95% that satisfies the positive and negative rotation standby constraints.
TABLE 1CSP Power station parameters
Under the above calculation conditions, the results of the day-ahead scheduling calculation of the price type demand response and the wind power consumption participated in by the CSP power station by applying the method of the invention are as follows:
1. uncertainty analysis of price type demand response and wind power forecast
In price type demand response, according to the psychological principle of consumers, when a scheduling center makes a day-ahead scheduling plan, the electricity utilization habits of users can be changed to a certain extent by means of real-time electricity prices with time scales of small levels, and therefore the purposes of cutting peaks, filling valleys and increasing wind power internet space are achieved, load response has uncertainty, and the influence of the uncertainty on scheduling results is analyzed in scheduling.
Electricity is a power supply which has volatility in output power and is not easy to control, and is a typical intermittent and low-power-density power supply, wind power prediction often has a certain prediction error, and uncertainty of the prediction error is described by normal distribution.
Establishment of CSP power station thermal-electric conversion model
The CSP power station components and energy flow diagram are shown in FIG. 2, the CSP power station containing the heat storage device mainly comprises a heat collection system, namely: the solar energy mirror field and the heat collecting tower, the heat storage device and the power generation system. The CSP electric station has good energy time shifting characteristics due to the arrangement of the heat storage device, and the defects of intermittency and fluctuation of solar energy are effectively overcome, so that the CSP electric station can be guaranteed to provide stable and controllable power supply. The CSP power station utilizes the heat collected by the solar mirror field to generate power through a certain energy flow exchange process and finally through Rankine cycle.
3. Establishment and solution of day-ahead scheduling model considering price type demand response and participation of CSP power station in wind power consumption
The method comprises the steps of comprehensively considering the power generation cost of a conventional thermal power generating unit, the operation and maintenance cost of a wind power and CSP power station, the renewable energy environmental benefit brought by grid-connected consumption, and the cost of the thermal power generating unit and the CSP power station participating in load standby and wind power prediction error standby, and establishing a combined scheduling model containing the wind power, the CSP power station and the thermal power station and based on optimal cost.
The price type demand response guides the user to change the electricity utilization behavior by changing the electricity price according to the economic principle, and has the functions of peak clipping and valley filling. The load before and after the demand response and the output situation of each type of unit are shown in fig. 3, and the real-time electricity price after the response is shown in fig. 4. As can be seen from fig. 3 and 4, the wind power has a back-peak regulation property, the price type demand response changes the electricity price at each moment, and the electricity usage habits of the users are influenced by the electricity price, so that the peak clipping and valley filling effects are achieved, and the grid-connected consumption of the wind power is increased to a certain extent. However, the thermal power generating unit has certain minimum output and still generates partial abandoned wind in the overlapping time period of the load valley and the wind power high-power generation due to the limitation of the minimum output and the negative rotation standby constraint of the thermal power generating unit.
The output of the CSP power station provided with the heat storage device is flexible and controllable, the output of a thermal power generating unit can be reduced while the wind power anti-peaking property is compensated, and further the environmental benefit is increased. The heat charging and discharging conditions of the CSP power station heat storage device and the heat storage quantity in each scheduling period are shown in FIG. 5.
As can be seen from fig. 5, the CSP power station has the characteristic of energy time shifting, the heat storage device releases the heat stored in the last period of the previous scheduling day in the morning for the CSP power station to output, the heat storage is performed in the period of the day when the solar radiation resources are sufficient, and the stored heat is released at night, thereby ensuring the stable output of the CSP power station.
The output of the CSP power station is stable and controllable, the CSP power station is used for providing standby for wind power and load prediction errors, and the CSP power station makes up the defect of prediction precision through renewable energy sources and ensures the stable operation of a power grid. The specific thermal power consumption of the CSP power station on the scheduled day is shown in FIG. 6.
The terms of calculation, illustration and the like in the embodiments of the present invention are used for further description, are not exhaustive, and do not limit the scope of the claims, and those skilled in the art can conceive other substantially equivalent alternatives without creative efforts based on the teachings of the embodiments of the present invention, which are within the scope of the present invention.
Claims (1)
1. A day-ahead wind power consumption scheduling method considering price type demand response and participation of a CSP power station is characterized in that uncertainty and system safety constraint of wind power prediction and price type demand response are considered under the premise of considering comprehensive cost, and a scheduling model based on the CSP power station and the price type demand response and participating in wind power consumption is constructed; the model combines a CSP power station and a price type demand response with wind power optimization scheduling, improves the capacity of a system for absorbing wind power through coordinated scheduling of two sides of a source load, and specifically comprises the following steps:
1) uncertainty analysis of price type demand response and wind power forecast
(a) Uncertainty of price type demand response
In price type demand response, according to the psychological principle of consumers, when a scheduling center makes a day-ahead scheduling plan, the electricity utilization habits of users can be changed by means of real-time electricity prices with time scales of hour levels, and the purposes of peak clipping, valley filling and wind power online space increasing are achieved;
in the price type demand response, the influence of the electricity price change rate on the load response rate is expressed by using an elasticity coefficient, and the load response rate for a T period is modeled as an expression (1):
wherein: phi is a unit ofΔq,tFor the response rate of the load for the period t,
the change rate of the electricity price of the load in the time interval T, and T is equal to (1: T);
e is a price type demand elasticity matrix expressed as formula (2):
wherein: epsiloniiCoefficient of self-elasticity,. epsilonijFor cross elasticity coefficients, subscripts i and j denote the ith and jth scheduling periods, respectively;
the uncertainty degree of the load response rate is summarized by adopting a triangular membership function, and a fuzzy expression of the load response rate and a membership parameter have the following relationship and are calculated as a formula (3):
wherein:
is phiΔq,tThe fuzzy expression of (1);
φΔq1,t，φΔq2,t，φΔq3,tmembership degree parameter of load response rate in t time period;
δtfor the maximum error level of the predicted load response rate at time t, it is calculated as (5):
wherein: lambda1A proportionality coefficient before the electricity price factor is dominant;
is the inflection point of the rate of change of electricity price;
the load change rate is corresponding to the inflection point of the electricity price change rate;
the maximum/minimum value of the rate of change of the electricity price;
(b) uncertainty of wind power prediction
Wind power is a power supply which has volatility in output power and is difficult to control, and is a typical intermittent and low-power-density power supply, and prediction errors exist in wind power prediction; the uncertainty of the prediction error is described by a normal distribution, which is expressed as formula (6):
wherein: omegatWind power prediction error at the time t; n represents that the wind power prediction error obeys normal distribution;
predicting the power of the wind power at the time t; wmFor installations in wind farmsMachine capacity;
the actual output of the wind power plant is the sum of the predicted output of the wind power plant and the prediction error, and meanwhile, the actual output of the wind power plant does not exceed the installed capacity, and the calculation is as the formula (7):
wherein:
real output of the wind power plant at the time t is obtained;
2) establishment of CSP power station heat-electricity conversion model
The solar mirror field collects heat energy generated by solar radiation to the heat collection tower, and the heat energy collected by the heat collection tower is calculated as the formula (8):
wherein:
the heat energy collected by the heat collection tower at the moment t is obtained; etad-thTo the light-to-heat conversion efficiency; sSFIs the area of a solar mirror field; dtRepresenting the direct solar radiation index at the moment t;
the heat energy collected by the heat collecting tower can be stored in the heat storage device through the heat transfer fluid and also can be directly supplied to the power generation system for power generation, so that the heat energy collected by the heat collecting tower has the possibility of being discarded in order to ensure the stable operation of the CSP power station; meanwhile, the heat energy used for generating electricity by the power generation system can also be supplemented by the heat storage system, and is expressed as the formula (9):
wherein:
representing the heat storage device heat release efficiency;
represents the heat energy rejected by the heat collection tower at the moment t; eSURepresents the thermal energy consumed by the power generation system during startup; u. oftA start variable indicating the power generation system, 1 indicating that the power generation system is started at that time;
the heat storage amount of the heat storage device at each time is expressed by the following formula (10):
wherein:
respectively representing the heat storage amount of the heat storage device at the moment t and the moment t-1; etalossRepresenting the rate of heat loss from the heat storage device;
represents the charging efficiency of the heat storage device;
representing the charging power of the heat storage device;
representing the heat release power of the heat storage device; Δ t represents a time interval;
the thermodynamic dynamic differential equation is not considered in the static model of the CSP power station, and the power generation power and the heat energy consumption power of the CSP power station can be regarded as a linear relation and are calculated as the formula (11):
wherein:
representing the generated power of the CSP power station for supplying load at the time t; etaRs,uThe positive rotation standby coefficient of the CSP power station;
3) establishment of day-ahead scheduling model considering PDR and CSP power station participating in wind power consumption
a) Objective function
The method comprises the following steps of comprehensively considering the power generation cost of a conventional thermal power generating unit, the operation and maintenance cost of a wind power and CSP power station, the renewable energy environmental benefit brought by grid-connected consumption, the cost of the thermal power generating unit and the CSP power station participating in load standby and wind power prediction error standby, establishing a combined scheduling model containing the wind power, the CSP power station and the thermal power station and based on optimal cost, and calculating a target function as a formula (12):
min F＝D1+D2+D3+D4-D5 (12)
wherein: f is the comprehensive cost of the system when thermal power, wind power and CSP power stations participate in the optimized dispatching; d1The power generation cost when supplying load to the thermal power generating unit; d2The operation and maintenance cost of the wind power is reduced; d3The operation and maintenance cost for supplying power to the load by the CSP power station; d4The cost for the thermal power generating unit and the CSP power station to participate in load and wind power prediction error standby is saved; d5Environmental benefits brought to the power generation grid-connected consumption of wind power and CSP power stations;
the output of the thermal power generating unit is flexible and controllable, and the stable operation of a power grid can be ensured through reasonable optimized scheduling; in order to meet the load requirement in the scheduling process, output needs to be adjusted often, and even the unit needs to be scheduled for starting and stopping, and the power generation cost at this time includes the coal consumption cost of the thermal power unit and the start and stop cost of the unit, and is calculated as an equation (13) and an equation (14):
fi(PGi,t)＝aiPGi,t 2+biPGi,t+ci (14)
wherein: t is the total duration; n is a radical ofGThe number of the thermal power generating units is; f. ofiThe coal consumption cost of the thermal power generating unit i is obtained; pGi,tThe output of the thermal power generating unit i in the time period t is obtained; v isi,tV and vi,t-1Respectively the running states of the thermal power generating unit i at t and t-1, if vi,tV denotes unit operation as 1i,tWhen the unit is stopped, 0 represents the unit is stopped; siThe start-stop cost of the unit i is calculated; a isi，bi，ciThe coal consumption cost parameter is the coal consumption cost parameter of the thermal power generating unit i;
wind power generation belongs to renewable energy power generation, coal is not consumed in the power generation process, but due to uncertainty of wind speed, operation and maintenance cost can be generated in the power generation process of a fan; the operation and maintenance cost of the wind power and the wind power output power can be approximately regarded as a linear relation, and are calculated as an expression (15):
wherein: kWOperating and maintaining costs for the wind farm; pW,tGenerating power of the wind power plant at the time t;
the CSP power station operation and maintenance cost is approximately regarded as a linear function of the generated power, and is calculated as the formula (16):
wherein: kCSPThe unit operation and maintenance cost of the CSP power station is saved;
in order to deal with uncertainty and emergency of load and wind power prediction, the reserve capacity of rotation needs to be reserved, and the reserve cost of a thermal power unit and a CSP power station is calculated as a formula (17):
wherein:
indicating the participation of the CSP power station in the load standby,
Representing the cost coefficient of the CSP power station participating in wind power standby;
wind power, CSP power station renewable energy are incorporated into the power networks and are consumed and have reduced thermal power unit's the net volume of generating electricity, effectively reduce the emission of sulfur and nitrate pollutant, can bring environmental benefit, calculate as (18) formula:
wherein: pW,tPower is consumed for the grid connection of the wind power plant at the time t; rhoCSPEnvironmental benefit coefficient brought to CSP power station grid-connected consumption; rhoWEnvironmental benefit coefficient brought to wind power grid connection consumption;
b) system constraints
When the network transmission loss is not counted, the sum of the output power of each unit is equal to the value after the power grid load response change, and is expressed as a formula (19):
wherein: l istThe load power value before the system needs to respond at the time t;
the transmission capacity constraint of the transmission line is expressed as formula (21):
-Pij,max≤Bij(θi,t-θj,t)≤Pij,max (21)
wherein: pij,maxIs the maximum transmission capacity of the transmission line between nodes i and j; b isijIs the susceptance between nodes ij; thetai,tIs the voltage phase angle of node i; thetaj,tIs the voltage phase angle of node j;
the opportunity constraint is adopted to determine the wind power reserve capacity, the wind power reserve cost is reduced on the basis of ensuring the safety of a power grid, and the concrete constraint is expressed as a formula (22):
wherein: cr { } is a confidence expression;
the positive rotation reserve capacity of the thermal power generating unit i at the moment t is obtained;
the negative rotation reserve capacity of the thermal power generating unit i at the moment t is obtained;
positive rotation reserve capacity provided for CSP power station at time t,
The negative spinning reserve capacity provided for the CSP plant at time t,
μLis the load reserve factor; alpha and beta are respectively confidence coefficients meeting the positive and negative rotation standby constraints;
the heat storage device of the CSP power station has to be within the rated limit range, and the heat storage device cannot be simultaneously charged/discharged in each scheduling period, and the specific constraint is expressed as the following formula (23) and formula (24):
Pt chaPt dis＝0 (24)
wherein:
the maximum charging power of the heat storage system;
the maximum heat release power of the heat storage system;
the heat storage quantity of the CSP power station heat storage device is expressed as a formula (25):
wherein:
the minimum heat storage quantity of the heat storage device is obtained; xiTSThe maximum heat storage capacity of the heat storage device is expressed by the number of hours of full load;
the maximum output power of the CSP power station;
the specific constraint of the maximum/minimum output of the thermal power generating unit is expressed as a formula (26):
UGi,tPi，min≤PGi,t≤UGi,tPi，max (26)
wherein: u shapeGi,tThe method comprises the following steps that 1 represents operation, and 0 represents shutdown, wherein the thermal power generating unit i is in an operating state; pi,minThe minimum output of the thermal power generating unit i is obtained; pi,maxThe maximum output of the thermal power generating unit i is obtained;
the ramp rate constraint expression of the thermal power generating unit is as follows (27):
-rdi≤PGi,t-PGi,t-1≤rui (27)
wherein: r isuiThe upward climbing rate of the unit i is obtained; r isdiThe downward climbing speed of the unit i is obtained;
during day-ahead scheduling, the wind power on-line power cannot exceed the predicted value, and the specific expression is (28):
wherein:
the predicted output of the wind power plant at the time t is obtained;
the sum of the expected load response values after the demand response is zero in the whole scheduling period, and is expressed as formula (29):
considering the benefit of the user, the load variation needs to be limited, and the satisfaction degree of the power utilization mode and the satisfaction degree of the power utilization expense expenditure of the user are used as measurement indexes and are expressed as formulas (30) and (31):
wherein:
using electricity for userA minimum value of satisfaction;
the minimum value of the satisfaction degree of electricity cost expenditure;
the load value after the price type demand response is between the upper limit and the lower limit of the load value before the response, and the specific constraint expression is (33):
wherein: l ismaxIs the maximum value of the original load curve, LminIs the minimum value of the original load curve; etamax、ηminTaking the value eta for the peak-to-valley difference coefficient of the demand response loadmax≥1，ηmin≤1。
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